AI has become so popular in picking stocks that it’s become ineffective

So many baseball teams have adopted the kind of statistical analysis made famous by “Moneyball” that the game has become “plodding” and “never more beset by inaction,” according to a recent report in The Wall Street Journal,

Could something similar be happening on Wall Street, which has also been embracing big data to find undervalued names of a different sort?

One of the biggest trends among asset managers in recent years has been the use of artificial intelligence in managing investments. These companies have employed powerful computers to mine massive data sets—including corporate commentary, social media chatter, credit-card data, and other statistics difficult for humans to discern patterns—and then developing portfolios based on that analysis.

Quant-based investing has become a growing part of the market. A July report from J.P. Morgan estimated that 20% of equity-based assets under management—amounting to trillions of dollars—were in quantitative strategies, although it included “traditional multifactor and smart-beta strategies” in its definition of the category, as well as “the equity portion of cross-asset systematic portfolios.”

Smart-beta strategies are similar to quant in that they are designed to outperform an index and their holdings are neither chosen by individuals nor weighted by market value. However, rather than the portfolios being developed through advanced-computing capabilities, their holdings are determined by rules that tilt the portfolio to strategies like “value” or “momentum.”

The firms using these AI-like technologies range from new upstarts to the biggest names on Wall Street. In April, BlackRock Inc.
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—the world’s largest asset manager, with more than $5 trillion in assets—announced it would overhaul its actively managed equities business by putting a greater emphasis on computer models rather than human managers. It said it would shift a few billion in assets from its traditional active group to its quant team and said it would be lowering fees for those funds in the process.

Asset managers are expected to continue adopting advanced-computing technology in their portfolio construction, but the trend suggests a question that’s similar to the situation currently unfolding in the ballpark: if everyone is using AI, then does the benefit of using AI evaporate?

The rise of competing quant strategies “has helped drive down available alpha by increasing the overall wisdom of crowds and shrinking the available alpha through more efficient markets,” wrote Jordi Visser, chief investment officer at Weiss Multi-Strategy Advisers. “Alpha” refers to outperformance over a benchmark, and is typically seen as a primary goal of investment managers not plugged into a power outlet.

In a research report, Visser said he saw value in artificial intelligence, which he wrote was “already better than the brain at pattern recognition and the ability to go through large amounts of data and make immediate predictive decision in rational markets.” Furthermore, he predicted that AI would replace many human jobs in the future.

However, he added, “there is a big difference between all of these statements and my belief that currently within the asset management industry there is tremendous AI hype and we are seeing some sort of bubble in this AI arms race which will leave many receiving worthless results on their investments.”

There are signs this is proving to be the case. In June, Morgan Stanley noted that correlations in stock markets were falling, which means individual names are rising and falling on their own fundamentals, as opposed to simply moving in tandem with the broader market. Such an environment is theoretically good for stock pickers, however, “using traditional quant factors wasn’t effective for that purpose,” the firm wrote, noting that “a majority of the 25 valuation, fundamental and momentum factors we monitor delivered negative returns” over the month of May.

The investment bank said the rate of underperformance was “comparable to the low in late 1999,” while 72% of factors had failed in Europe over the month. “The rising popularity of quant techniques among investors could further reduce the alpha from traditional factors,” it concluded.

To be sure, some advocates of big-data strategies still see its potential, especially as it continues to grow, evolve, and learn over time.

“There’s still quite a range in AI models—what data you’re looking at, where you get it, how you scrub it, what you’re looking for. There could be other quant groups that are looking at the same raw data but analyzing it in a different way, meaning the same input material can result in different insights and outcomes,” said Jamie Wise, chief executive officer of Buzz Indexes.

Buzz is the sponsor of the Buzz US Sentiment Leaders ETF
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an exchange-traded fund that selects its holdings based on positive chatter in social media and other online sources. The fund is up 17.2% in 2017, above the 13.9% rise of the S&P 500
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Wise said the market was “still in the early days” of AI adoption, and that he didn’t believe it was already so efficient as to be useless in discovering undervalued names, something he suggested may have been true of algorithmic trading a decade ago.

“Back then, everyone was looking at historical prices and correlations and divergences that were supposed to converge again; it was more likely that people would be looking at the same things, rending them less effective,” he said. “At some point AI-based data will be more commoditized, but it will still be only one part of the investment decision, one of billions of pieces moving at once.”

He added that this “won’t mean answers about how to invest, it will just lead to asking more questions. That’s a healthy, natural evolution of the tool.”

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